Logistic growth curve analysis in associative learning data

S. M. Hartz, Y. Ben-Shahar, M. Tyler

Research output: Contribution to journalArticle

25 Scopus citations

Abstract

We propose an alternative statistical method, logistic growth curve analysis, for the analysis of associative learning data with two or more comparison groups. Logistic growth curve analysis is more sensitive and easier to interpret than previously published methods such as χ2 or ANOVA, which require the data to be collapsed into individual total scores or proportion of responses over time. Additionally, this type of analysis better fits the typical graphical representation of associative learning data. An analysis is presented where associative learning data from honeybees are analyzed using the three techniques, and the accessibility and power of the logistic growth curve analysis is highlighted.

Original languageEnglish
Pages (from-to)185-189
Number of pages5
JournalAnimal Cognition
Volume3
Issue number4
DOIs
StatePublished - Mar 2001
Externally publishedYes

Keywords

  • Associative learning
  • Comparison of statistical methods
  • Honeybees
  • Logistic growth curve analysis

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